Observer-based Quasi-Synchronization Control for Master-Slave Neural Networks under Sojourn-Probability-based Stochastic Communication Protocol

• Published in: IEEE transactions on network science and engineering Vol. 10; no. 6; pp. 1 - 10
• Main Authors: Zhao, Xia, Qu, Fanrong, Liu, Chunsheng, Tian, Engang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biological neural networks; Control systems design; Control theory; Linear matrix inequalities; Main-secondary; master-slave neural networks; Multi-layer neural network; Neural networks; Observers; Probability theory; Quasi-synchronization; sojourn probability; stochastic communication protocol; Switches; Synchronism; Synchronization; Upper bounds; Variance
• ISSN: 2327-4697; 2334-329X
• DOI: 10.1109/TNSE.2023.3267072

This paper investigates the observer-based quasi-synchronization control for a class of master-slave neural networks under sojourn-probability-based (SP-based) stochastic communication protocol (SCP). The novel scheduling protocol, is introduced to determine which sensor node obtains the transmission right, and the transmission probabilities of SCP are determined by the alleged SP. The primary control objectives are threefold: 1) designing proper observer-based controller such that the quasi-synchronization error be bounded in the mean-square sense; 2) letting the estimation error of the master neural network constrained within specified variance bounds; and 3) finding the optimized quasi-synchronization error and estimation error variance upper bounds. To achieve these aims, the recursive matrix inequality approach and stochastic analysis method are utilized, and a quasi-synchronization control algorithm is developed to ensure the pre-specified quasi-synchronization performance under the proposed SP-based SCP. In addition, two simulations are employed to validate the efficiency and practicality of the designed scheme.